{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 1: Training a spiking neural network with surrogate gradients\n",
    "\n",
    "Friedemann Zenke (https://fzenke.net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For more details on surrogate gradient learning, please see: \n",
    "> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.\n",
    "> https://arxiv.org/abs/1901.09948"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction \n",
    "\n",
    "The last months have seen a surge of interest in training spiking neural networks to do meaningful computations. On the one hand, this surge was fueled by the limited accomplishment of more traditional, and often considered more biologically plausible, learning paradigms in creating functional neural networks that solve interesting computational problems. This limitation was met by the undeniable success of deep neural networks in acing a diversity of challenging computational problems. A success that has raised both the bar and the question of how well this progress would translate to spiking neural networks.\n",
    "\n",
    "The rise of deep learning over the last decade is in large part due to GPUs and their increased computational power, growing training data sets, and --- perhaps most importantly --- advances in understanding the quirks and needs of the error back-propagation algorithm. For instance, we now know that we have to avoid vanishing and exploding gradients, a feat that can be accomplished by choice of a sensible nonlinearity, proper weight initialization, and a suitable optimizer. Powerful software packages supporting auto-differentiation have since made mangling with deep neural networks a breeze in comparison to what it used to be. This development begs the question of how much of this knowledge gain from deep learning and its tools we can leverage to train spiking neural networks. Although a complete answer to these questions cannot be given at the moment, it seems that we can learn a lot.\n",
    "\n",
    "In this tutorial, we use insights and tools from machine learning to build, step-by-step, a spiking neural network. Explicitly, we set out with the goal of building networks that solve (simple) real-world problems. To that end, we focus on classification problems and use supervised learning in conjunction with the aforementioned back-propagation algorithm. To do this, we have to overcome a vanishing gradient problem caused by the binary nature of the spikes themselves.\n",
    "\n",
    "In this tutorial, we will first show how a simple feed-forward spiking neural network of leaky integrate-and-fire (LIF) neurons with current-based synapses can be formally mapped to a discrete-time recurrent neural network (RNN). We will use this formulation to explain why gradients vanish at spikes and show one way of how the problem can be alleviated. Specifically, we will introduce surrogate gradients and provide practical examples of how they can be implemented in PyTorch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping LIF neurons to RNN dynamics\n",
    "\n",
    "The de-facto standard neuron model for network simulations in computational neuroscience is the LIF neuron model which is often formally written as a time continuous dynamical system in differential form:\n",
    "$$\\tau_\\mathrm{mem} \\frac{\\mathrm{d}U_i^{(l)}}{\\mathrm{d}t} = -(U_i^{(l)}-U_\\mathrm{rest}) + RI_i^{(l)}$$\n",
    "where $U_i$ is the membrane potential of neuron $i$ in layer $l$, $U_\\mathrm{rest}$ is the resting potential, $\\tau_\\mathrm{mem}$ is the membrane time constant, $R$ is the input resistance, and $I_i$ is the input current. The membrane potential $U_i$ characterizes the hidden state of each neuron and, importantly, it is not directly communicated to downstream neurons. However, a neuron fires an action potential or spike at the time $t$ when its membrane voltage exceeds the firing threshold $\\vartheta$. After having fired a spike, a neurons membrane voltage is reset $U_i \\rightarrow U_\\mathrm{rest}$. We write\n",
    "$$S_i^{(l)}(t)=\\sum_{k \\in C_i^l} \\delta(t-t_j^k)$$ \n",
    "for the spike train (ie. the sum of all spikes $C_i^l$ emitted by neuron $i$ in layer $l$). Here $\\delta$ is the Dirac delta function and $t_i^k$ are the associated firing times of the neuron.\n",
    "\n",
    "Spikes travel down the axon and generate a postsynaptic currents in connected neurons. Using our above formalism we can thus write\n",
    "$$\\frac{\\mathrm{d}I_i}{\\mathrm{d}t}= -\\frac{I_i(t)}{\\tau_\\mathrm{syn}} + \\sum_j W_{ij} S_j^{(0)}(t) + \\sum_j V_{ij} S_j^{(1)}(t)$$\n",
    "where we have introduced the synaptic weight matrices $W_{ij}$ (feed-forward), $V_{ij}$ (recurrent), and the synaptic decay time constant $\\tau_\\mathrm{syn}$.\n",
    "\n",
    "To link to RNNs apparent, we will now express the above equations in discrete time. In the interest of brevity we switch to natural units $U_\\mathrm{rest}=0$, $R=1$, and $\\vartheta=1$. Our arguments remain unaffected by this choice, and all results can always be re-scaled back to physical units. To highlight the nonlinear character of a spike, we start by noting that we can set\n",
    "$$S_i^{(l)}(t)=\\Theta(U_i^{(l)}(t)-\\vartheta)$$\n",
    "where $\\Theta$ denotes the Heaviside step function.\n",
    "\n",
    "Assuming a small simulation time step of $\\Delta_t>0$ we can approximate the synaptic dynamics by\n",
    "$$I_i^{(l)}(t+1) = \\alpha I_i^{(l)}(t) + \\sum_j W_{ij} S_j^{(l-1)}(t) +\\sum_j V_{ij} S_j^{(l)}(t)$$\n",
    "with the constant $\\alpha=\\exp\\left(-\\frac{\\Delta_t}{\\tau_\\mathrm{syn}} \\right)$. Further, the membrane dynamics can be written as\n",
    "$$U_i^{(l)}(t+1) = \\underbrace{\\beta U_i^{(l)}(t)}_{\\mathrm{leak}} + \\underbrace{I_i^{(l)}(t)}_{\\mathrm{input}} -\\underbrace{S_i^{(l)}(t)}_{\\mathrm{reset}}$$\n",
    "with the output $S_i(t) = \\Theta(U_i(t)-1)$ and the constant $\\beta=\\exp\\left(-\\frac{\\Delta_t}{\\tau_\\mathrm{mem}}\\right)$. Note the distinct terms on the right-hand-side of the equation which are responsible individually for i) leak, ii) synaptic input, and iii) the spike reset.\n",
    "\n",
    "\n",
    "\n",
    "These equations can be summarized succinctly as the computational graph of an RNN with a specific connectivity structure. \n",
    "<img src=\"figures/snn_graph/snn_graph.png\" width=\"450\">\n",
    "Time flows from left to right. Inputs enter the network at each time step from the bottom of the graph ($S_i^{(0)}$). These inputs sequentially influence the synaptic currents $I_i^{(1)}$, membrane potentials the $U_i^{(1)}$, and finally the spiking output $S_i^{(1)}$.  Moreover, dynamic quantities have direct input on future time steps. We have suppressed the indices $i$ in the figure for clarity.\n",
    "\n",
    "The computational graph illustrates a concept which is known as unrolling in time, which emphasizes the duality between a deep neural network and a recurrent neural network, which is nothing more but a deep network in time (with tied weights). Due to this fact, we can train RNNs using the back-propagation of error through time (BPTT). We will discuss problems arising from the binary character of the spiking nonlinearity later. For now, let us start by implementing the above dynamics in a three-layer spiking neural network in PyTorch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with a simple multilayer network model with a single hidden layer, as shown below. For simplicity, we will not use recurrent connections $V$ for now, keeping in mind that they can be added later should the need arise.\n",
    "\n",
    "<img src=\"figures/mlp_sketch/mlp_sketch.png\">\n",
    "\n",
    "For the sake of argument, we set the numbers for the input, hidden and output neurons as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_inputs  = 100\n",
    "nb_hidden  = 4\n",
    "nb_outputs = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have seen above, we are technically simulating an RNN. Thus we have to simulate our neurons for a certain number of timesteps. We will use 1ms timesteps, and we want to simulate our network for say 200 timesteps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 1e-3\n",
    "nb_steps  = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To take advantage of parallelism, we will set up our code to work on batches of data like this is usually done for neural networks that are trained in a supervised manner.\n",
    "To that end, we specify a batch size here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these basic design choices made, we can now start building the actual network. Here we will be using PyTorch, but you will be able to reproduce these results in most common machine learning libraries.\n",
    "\n",
    "We start by importing the libraries we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import seaborn as sns\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = torch.float\n",
    "device = torch.device(\"cpu\")\n",
    "\n",
    "# Uncomment the line below to run on GPU\n",
    "# device = torch.device(\"cuda:0\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A simple synthetic dataset \n",
    "\n",
    "We start by generating some random spiking data set, which we will use as input to our network. In the beginning, we will work with a single batch of data. It will be straight forward to expand later what we have learned to larger datasets.\n",
    "\n",
    "Suppose we want our network to classify a set of different sparse input spike trains into two categories. \n",
    "\n",
    "To generate some synthetic data, we fill a tensor of (batch_size x nb_steps x nb_inputs) with random uniform numbers between 0 and 1 and use this to generate our input dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = 5 # Hz\n",
    "prob = freq*time_step\n",
    "mask = torch.rand((batch_size,nb_steps,nb_inputs), device=device, dtype=dtype)\n",
    "x_data = torch.zeros((batch_size,nb_steps,nb_inputs), device=device, dtype=dtype, requires_grad=False)\n",
    "x_data[mask<prob] = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the plot the spike raster of the first input pattern, this synthetic dataset looks as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_id = 0\n",
    "plt.imshow(x_data[data_id].cpu().t(), cmap=plt.cm.gray_r, aspect=\"auto\")\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Unit\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we assign a random label of 0 or 1 to each of our input patterns. Our network's task will be to differentiate these patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = torch.tensor(1*(np.random.rand(batch_size)<0.5), device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that there is no structure in the data (because it is entirely random). Thus we won't worry about generalization now and only care about our ability to overfit these data with the spiking neural network we are going to build in a jiffy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup of the spiking network model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now is the time to implement our LIF neuron model in discrete time.\n",
    "We will first do this step by step before we wrap all the steps into a function later on.\n",
    "But first, we fix several model constants such as the membrane and the synaptic time constant. Moreover, we define some essential variables, including our $\\alpha$ and $\\beta$ as described above. We do this now because we will use some of these variables to scale our weights to meaningful ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tau_mem = 10e-3\n",
    "tau_syn = 5e-3\n",
    "\n",
    "alpha   = float(np.exp(-time_step/tau_syn))\n",
    "beta    = float(np.exp(-time_step/tau_mem))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we set up our weight matrices, which connect the input and the hidden layer, as well as the matrix connecting the hidden layer with the output layer. Moreover, we initialize these weights randomly from a normal distribution. Note that we scale the variance with the inverse square root of the number of input connections. Moreover, for the sake of simplicity, we ignore Dale's law in this tutorial. Thus weights can be either excitatory or inhibitory. This choice is prevalent in artificial neural networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init done\n"
     ]
    }
   ],
   "source": [
    "weight_scale = 7*(1.0-beta) # this should give us some spikes to begin with\n",
    "\n",
    "w1 = torch.empty((nb_inputs, nb_hidden),  device=device, dtype=dtype, requires_grad=True)\n",
    "torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\n",
    "\n",
    "w2 = torch.empty((nb_hidden, nb_outputs), device=device, dtype=dtype, requires_grad=True)\n",
    "torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\n",
    "\n",
    "print(\"init done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A spiking neuron model in discrete time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we need to do to implement our spiking neuron is to multiply all input spikes with the weight matrix. We have to do this for each time step in each input example in the batch. Because we have stored our input spikes in a rank three tensor we can express this operation in a single line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "h1 = torch.einsum(\"abc,cd->abd\", (x_data, w1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These \"weighted\" input spikes will now feed into our synaptic variable and, ultimately, the membrane potential. To trigger a spike, we need to define moreover a threshold or spike function, which we do in the following. We will later have to alter this definition to train the network, but more about that later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The spiking nonlinearity (the naive way)\n",
    "\n",
    "In discrete-time, as explained earlier, we can formulate our spiking nonlinearity as a Heaviside step function. So let's begin with defining a Heaviside function. One way of implementing it is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spike_fn(x):\n",
    "    out = torch.zeros_like(x)\n",
    "    out[x > 0] = 1.0\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each trial, we initialize the synaptic currents and membrane potentials at zero.\n",
    "Next, we need to implement a loop that simulates our neuron models over time. \n",
    "Moreover, we will record the membrane potentials and output spikes of all trials and all neurons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n",
    "mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n",
    "\n",
    "# Here we define two lists which we use to record the membrane potentials and output spikes\n",
    "mem_rec = []\n",
    "spk_rec = []\n",
    "\n",
    "# Here we loop over time\n",
    "for t in range(nb_steps):\n",
    "    mthr = mem-1.0\n",
    "    out = spike_fn(mthr)\n",
    "    rst = out.detach() # We do not want to backprop through the reset\n",
    "\n",
    "    new_syn = alpha*syn +h1[:,t]\n",
    "    new_mem = (beta*mem +syn)*(1.0-rst)\n",
    "    \n",
    "    mem_rec.append(mem)\n",
    "    spk_rec.append(out)\n",
    "    \n",
    "    mem = new_mem\n",
    "    syn = new_syn\n",
    "\n",
    "# Now we merge the recorded membrane potentials into a single tensor\n",
    "mem_rec = torch.stack(mem_rec,dim=1)\n",
    "spk_rec = torch.stack(spk_rec,dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that's it. The above loop has now simulated our neurons for '''nb_steps''' and stored their membrane traces and output spikes. Let us take a look at those membrane potentials in which we directly \"paste\" the spikes for visual inspection. We will directly plot multiple trials at once and define a little helper function for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_voltage_traces(mem, spk=None, dim=(3,5), spike_height=5):\n",
    "    gs=GridSpec(*dim)\n",
    "    if spk is not None:\n",
    "        dat = 1.0*mem\n",
    "        dat[spk>0.0] = spike_height\n",
    "        dat = dat.detach().cpu().numpy()\n",
    "    else:\n",
    "        dat = mem.detach().cpu().numpy()\n",
    "    for i in range(np.prod(dim)):\n",
    "        if i==0: a0=ax=plt.subplot(gs[i])\n",
    "        else: ax=plt.subplot(gs[i],sharey=a0)\n",
    "        ax.plot(dat[i])\n",
    "        ax.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(dpi=100)\n",
    "plot_voltage_traces(mem_rec, spk_rec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our random initialization gives us some sporadic spiking. Thus far, we have only an input layer and a spiking layer, which should become our hidden layer. Next, we will have to add a readout layer to our network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding a readout layer\n",
    "\n",
    "To use our network as a classifier, we need to have a readout layer on whose output we can define a cost function. There are several possibilities for doing this. For instance, we could count output layer spikes, or we could directly define an objective function on the membrane potential of the output neurons. Here we will follow the latter approach, but keep in mind that there are many other possibilities of defining an output layer and respective cost functions on them.\n",
    "\n",
    "In the following, we will build the output layer as a population of leaky integrator neurons. The reason for this choice is that leaky integration is the natural way of how neurons receive the spiking output of their brethren. Moreover, because we will need this code again, we combine our code from above plus the added readout layer into a single function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_snn(inputs):\n",
    "    h1 = torch.einsum(\"abc,cd->abd\", (inputs, w1))\n",
    "    syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n",
    "    mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n",
    "\n",
    "    mem_rec = []\n",
    "    spk_rec = []\n",
    "\n",
    "    # Compute hidden layer activity\n",
    "    for t in range(nb_steps):\n",
    "        mthr = mem-1.0\n",
    "        out = spike_fn(mthr)\n",
    "        rst = out.detach() # We do not want to backprop through the reset\n",
    "\n",
    "        new_syn = alpha*syn +h1[:,t]\n",
    "        new_mem = (beta*mem +syn)*(1.0-rst)\n",
    "\n",
    "        mem_rec.append(mem)\n",
    "        spk_rec.append(out)\n",
    "        \n",
    "        mem = new_mem\n",
    "        syn = new_syn\n",
    "\n",
    "    mem_rec = torch.stack(mem_rec,dim=1)\n",
    "    spk_rec = torch.stack(spk_rec,dim=1)\n",
    "\n",
    "    # Readout layer\n",
    "    h2= torch.einsum(\"abc,cd->abd\", (spk_rec, w2))\n",
    "    flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n",
    "    out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n",
    "    out_rec = [out]\n",
    "    for t in range(nb_steps):\n",
    "        new_flt = alpha*flt +h2[:,t]\n",
    "        new_out = beta*out +flt\n",
    "\n",
    "        flt = new_flt\n",
    "        out = new_out\n",
    "\n",
    "        out_rec.append(out)\n",
    "\n",
    "    out_rec = torch.stack(out_rec,dim=1)\n",
    "    other_recs = [mem_rec, spk_rec]\n",
    "    return out_rec, other_recs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run this code and plot the output layer \"membrane potentials\" below. As desired, these potentials do not have spikes riding on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_rec,other_recs = run_snn(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(dpi=100)\n",
    "plot_voltage_traces(out_rec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By preventing the output neurons from spiking themselves, we can define a relatively smooth objective on their membrane voltages directly. Specifically, we use the maximum voltage over time of each output unit\n",
    "$$\\hat U^\\mathrm{out}_i=\\max_t U^\\mathrm{out}_i(t)$$\n",
    "and then use this vector as input for either an argmax to compute the classification accuracy or as we will see below as input for a standard softmax function in conjunction with a negative log-likelihood loss for optimizing the weights in the network. \n",
    "\n",
    "Let us first compute the classification accuracy of this random network. We will see that this accuracy is somewhere around 50% as it should be since that corresponds to the chance level of our synthetic task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.516\n"
     ]
    }
   ],
   "source": [
    "def print_classification_accuracy():\n",
    "    \"\"\" Dirty little helper function to compute classification accuracy. \"\"\"\n",
    "    output,_ = run_snn(x_data)\n",
    "    m,_= torch.max(output,1) # max over time\n",
    "    _,am=torch.max(m,1) # argmax over output units\n",
    "    acc = np.mean((y_data==am).detach().cpu().numpy()) # compare to labels\n",
    "    print(\"Accuracy %.3f\"%acc)\n",
    "    \n",
    "print_classification_accuracy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised learning\n",
    "\n",
    "So far, we have built the infrastructure to simulate our spiking neural network, but we have worked with purely random network weights thus far.\n",
    "The vanilla method to adjust network weights to decrease the specified objective is gradient descent. \n",
    "Machine learning libraries like Tensorflow and PyTorch make implementing gradient descent a breeze.\n",
    "We first perform gradient descent on the correct gradient and use this as a motivation for introducing surrogate gradients.\n",
    "Here we go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised learning with the true gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = [w1,w2] # The paramters we want to optimize\n",
    "optimizer = torch.optim.Adam(params, lr=2e-3, betas=(0.9,0.999)) # The optimizer we are going to use\n",
    "\n",
    "log_softmax_fn = nn.LogSoftmax(dim=1) # The log softmax function across output units\n",
    "loss_fn = nn.NLLLoss() # The negative log likelihood loss function\n",
    "\n",
    "# The optimization loop\n",
    "loss_hist = []\n",
    "for e in range(1000):\n",
    "    # run the network and get output\n",
    "    output,_ = run_snn(x_data) \n",
    "    # compute the loss\n",
    "    m,_=torch.max(output,1)\n",
    "    log_p_y = log_softmax_fn(m) \n",
    "    loss_val = loss_fn(log_p_y, y_data)\n",
    "\n",
    "    # update the weights\n",
    "    optimizer.zero_grad()\n",
    "    loss_val.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    # store loss value\n",
    "    loss_hist.append(loss_val.item())\n",
    "    \n",
    "loss_hist_true_grad = loss_hist # store for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_hist)\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.512\n"
     ]
    }
   ],
   "source": [
    "print_classification_accuracy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We appreciate that loss decreases over iterations and converges towards a steady state. The classification accuracy, however, does not seem to improve dramatically throughout the optimization. What a shame! \n",
    "\n",
    "The underlying reason is that the nonlinearity of the hidden units have zero derivatives everywhere except at threshold crossings, where they become infinite. In practice that means that weight updates in the hidden layer vanish and the weights remain unmodified. By plotting the hidden layer activations and comparing them with what we have plotted before, we will see that these activations have not changed at all. Thus no learning happens in the hidden layer. The reason why the loss decreased initially during optimization is that the output layer weights could still change and allow for some improvement (even if it was very little).\n",
    "\n",
    "To improve performance, we need to get the hidden layer units to take part in learning. To achieve this, we will introduce a surrogate gradient in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "output,other_recordings = run_snn(x_data)\n",
    "mem_rec, spk_rec = other_recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(dpi=100)\n",
    "plot_voltage_traces(mem_rec, spk_rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(dpi=100)\n",
    "plot_voltage_traces(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised learning with surrogate gradients\n",
    "\n",
    "In the last section, we saw that the hidden layer units did not participate.\n",
    "The underlying reason is that the partial derivative of the step function we used has a vanishing derivative everywhere (except at zero where it becomes infinite).\n",
    "\n",
    "Most conventional neural networks avoid this problem by choosing a nonlinearity with non-zero partial derivative. For instance, sigmoidal or tanh units were standard during the beginnings of neural networks research. Today, ReLUs are more common. Importantly, all these activation functions have substantial non-zero support, which allows gradients to flow (to a greater or lesser extent).\n",
    "\n",
    "What do we if we want to stick to our binary nonlinearity? There have been several approaches to tackle this problem. Here we use one such strategy which has been applied successfully to spiking neural networks: We use a surrogate gradient approach.\n",
    "\n",
    "The idea behind a surrogate gradient is dead simple. Instead of changing the nonlinearity itself, we only change the gradient. Thus we use a different \"surrogate\" gradient to optimize parameters that would otherwise have a vanishing gradient.\n",
    "\n",
    "<img src=\"figures/surrgrad/surrgrad.png\" width=\"450\">\n",
    "Specifically, we use the partial derivative of a function which to some extent approximates the stepfunction $\\Theta(x)$.\n",
    "In what follows, chiefly, we will use (up to rescaling) the partial derivative of a fast sigmoid function $\\sigma(x)$. \n",
    "While $\\Theta$ is invariant to multiplicative rescaling, $\\sigma$ isn't. Thus we have to introduce a scale parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SurrGradSpike(torch.autograd.Function):\n",
    "    \"\"\"\n",
    "    Here we implement our spiking nonlinearity which also implements \n",
    "    the surrogate gradient. By subclassing torch.autograd.Function, \n",
    "    we will be able to use all of PyTorch's autograd functionality.\n",
    "    Here we use the normalized negative part of a fast sigmoid \n",
    "    as this was done in Zenke & Ganguli (2018).\n",
    "    \"\"\"\n",
    "    \n",
    "    scale = 100.0 # controls steepness of surrogate gradient\n",
    "\n",
    "    @staticmethod\n",
    "    def forward(ctx, input):\n",
    "        \"\"\"\n",
    "        In the forward pass we compute a step function of the input Tensor\n",
    "        and return it. ctx is a context object that we use to stash information which \n",
    "        we need to later backpropagate our error signals. To achieve this we use the \n",
    "        ctx.save_for_backward method.\n",
    "        \"\"\"\n",
    "        ctx.save_for_backward(input)\n",
    "        out = torch.zeros_like(input)\n",
    "        out[input > 0] = 1.0\n",
    "        return out\n",
    "\n",
    "    @staticmethod\n",
    "    def backward(ctx, grad_output):\n",
    "        \"\"\"\n",
    "        In the backward pass we receive a Tensor we need to compute the \n",
    "        surrogate gradient of the loss with respect to the input. \n",
    "        Here we use the normalized negative part of a fast sigmoid \n",
    "        as this was done in Zenke & Ganguli (2018).\n",
    "        \"\"\"\n",
    "        input, = ctx.saved_tensors\n",
    "        grad_input = grad_output.clone()\n",
    "        grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\n",
    "        return grad\n",
    "    \n",
    "# here we overwrite our naive spike function by the \"SurrGradSpike\" nonlinearity which implements a surrogate gradient\n",
    "spike_fn  = SurrGradSpike.apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init done\n"
     ]
    }
   ],
   "source": [
    "# The following lines will reinitialize the weights\n",
    "torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\n",
    "torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\n",
    "print(\"init done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = [w1,w2]\n",
    "optimizer = torch.optim.Adam(params, lr=2e-3, betas=(0.9,0.999))\n",
    "\n",
    "log_softmax_fn = nn.LogSoftmax(dim=1)\n",
    "loss_fn = nn.NLLLoss()\n",
    "\n",
    "loss_hist = []\n",
    "for e in range(1000):\n",
    "    output,_ = run_snn(x_data)\n",
    "    m,_=torch.max(output,1)\n",
    "    log_p_y = log_softmax_fn(m)\n",
    "    loss_val = loss_fn(log_p_y, y_data)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss_val.backward()\n",
    "    optimizer.step()\n",
    "    loss_hist.append(loss_val.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 495x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.3,2),dpi=150)\n",
    "plt.plot(loss_hist_true_grad, label=\"True gradient\")\n",
    "plt.plot(loss_hist, label=\"Surrogate gradient\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "output,other_recordings = run_snn(x_data)\n",
    "mem_rec, spk_rec = other_recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(dpi=100)\n",
    "plot_voltage_traces(mem_rec, spk_rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9wR6suWScH++Qcx94MLfrY4ddfl/JDeimX/TD+J7OaweMdVY0us6qHYexbpMRztoxQ2geWktropaWRK3TGq+jJebgE8N3TJrDwXdi+NjtxLr4S3YnQStxx+e61kP/d3nrqdcD/8o0NS6w1bmbAJt1eE3B1Aw9g3kEAuZ8GoW5AI62r2A5kWlq3LXXGevBS9/bZfnWztyipZ9V1HNly8l/+/GVPzixlGMlU+mJE1h63laxeV/cwpk3cZKzhHHOcsaxnKFOM0Nopp711DvrqaENh9yGv7f5f4Dg/6Sv/w8D6Y9tn5p+5sw//7XnPUt3z3cPfPaTsRc/Us5jdie4rl3YctZfbmnb/1HMBWIoXVzTgOEuq0aelrh7kyNjj4/cMjY/XvzIPWvzO16vnC6WOzLv3d627/UnzPxnr/pQ97rO3cFPBH+kgXZ8/OEu82f7Hn41mUqfD+wEfNQht8shsWd2OT7+yA67x14f3eCsjs33R2UnXprpfWA040SfAJwMHADUxR2f3Zy3iu7+am4qmGE5g6E5rwD+ZD9fTk53f/85uXG85G9R8lSNCxjDla0nc2XrSSSdBewRe50tnPls5ixkU2cpI1nNCGctI1jDEKf4SKUxp6sLUbgXqISf6/OPsTu1tJJwOp9Su8de37unzyZTaefriVvO3s5577xf1ry19VhnRajP89f7ce5s22df7AxPyVQ6i3l81N0N2gGlHj+ZSsczTY1lLXEkyMW7+g3U0cLpiX8Pyw+N3AXTp/ULmXqOx/xmo/oor7/Kfn7Fuvn7D5SsP5T72j4yHZhe0v4M52etx/MzjmdTFrNHbDZbxeaxmbOIyc6HjGQNI5w1jGANw5zmLo8T73Rt6901zcHv9TWo10F5PmO++evWo67u7ed6Y0tn7thD4s+mR7GqHs+Nd1eNYEuis/DcVkwjjnYNwCY6y0bhuWPwsqVXX3vuUZh5O6cUbF2A6Zo1G1OttQhTBbIKWH1l68nLMHN+TsE8T9uT0ifBKNka6u/8detRRbtzrPHraq9vO2THFhK7Y0o0fpA/m64qsm6XnTUZf2Jrpm1icNb5BS8Av5YWfwRrnAnOsvhoZ2V8FCvjrrM6NpLVnYZVjJPrdDGI0f62M14ksMVK+FypfJzV2/Xlgz34bdsR7f7+NbROODNx1z/HOStiTRd/aVzqimuKtmFouvhLx91a++x1u8fe3FDKW+8nyPibrF7kN8xbxZCFa6ibu95PLGglvqKNWLbNpCuBFh+nDWjzodUuB2kbkPPNqFVd6PzHzuE4/2zbJ/mGP2Vv4BPARzDnDfaYc4E5Ba/3MVV7n8AMZ9mGeaSx1L6WFFkuuwdyu+39YG7XTo+Kjo7/76hJzpKLd4i913WANY9tLgAuJv9vBfNvm4Wp6VpgX1nMFJBr7KtllV/vL/FHxpcwMr7UHxlvI+a3Emc9Cb/Vj+daiZMj5udwnBwxB6DNxsUcMSeH4wTLPf07c8T6fVfbQqLs16DX/KnHvNE6ZXzPe5ZHC4n4za0HbruMkXti2g34mP+Pbq5n+fV5jG29I7cf5Npd09pd50awhgnOssQmztIN17XhrE0E16PC61nh9Si4hsW6eL+VeK/nQ4jkM2U7K0gzZiCAyXay9e72PxX4HaZx2FrMAPo3YaqQxwN742WfLOF7E5hgHPR7m4/p3nEb8FqnUYy6YZ9Z1dmxtGUjt+KSibmRzhrn161HHnf2zJv+XvheMpV2vMQNt50Yf/DYIc56Wvw4T+e2eeN9f/y1OZxffm7m7WvDynehZCo9GjM07XLgw0xTY2u4Oeolzz0AeAB4Ey+7dZH3J2G6RO1pt8zGjHR3O1427MlkRIA+lJQrwsu24bnzMKXOybQfx7bDvu6XyA8heRfwFbzsHPvea5igPA3TgKOb73TjwJ8xz6iDKQ8vtZ3Te81e0AbXRU36bKE/au1IZ83QYTTvgbnwA+a5cVPi0SemJx7aHeClXHLB7NxmR3125p1Ph5bZLmSaGoNS7mAVPFvaHM9N2Ba5huduB/wHc01ZDnwduEGNFCVqohmUjfcxP6ApdBVQPfcQzIAAYFpkf6vDj+xN4JOYoNyTqzABeT1wAl72jr5lW6rREkYunsbczYY7a9sNyXp47IkHg4D8UNvOd+0ff/HInS6bpUAwMOZiatjqMA3T3gHAcycD92KGYnwdOMxOsiESOVFu3PCBTYuPd+254zF9bGPAdcCFRe5637TpNt1+k+ceirlzBjhVAVl6a4U/dA6Ay6ptg20/vfj0a06J3/9xgIfadvnz/pf/t1ElswFk/rav27VdzTbXBf6NCcivAfsqIEuUDYagPKWL93+Eef71MnBuF897n7Ppvl2OWuW5w4Br7drVeNmydh+Q6rCSoQ8BTHUWJZOptDPj4vO/emYifVbM8Xkqt82T+8dnlbsVvhT3P5seiOfWA/8AdsQ03DqsVw0+RUIQ5aD8vk07l5Q9dw/g83btdLzsuk77GI9jqrMmA13No3oh5i46Q2nTNIp0Us/669p8h2mxubUnxB+6/pzEHT8b7qxjdm7KvFGs+nhvGglKv6RterJdPgDTCvdwNeaSwSDKQbm76uvLbHojXrbrBjNmSMlgBJib8dz2XYk8dxz5GW++1ddGXSKNl9+Tme1vNhfgqprfnjrRWerM9cescvB3nXbZq5Xt1Fnd7sEM3tMAHIhpI3I0Xvb5MDMlUqooB+WgpNy++tpzdwYOxbSQ9ko4zrcxraCnYbpJFfoqZmSYZzHdp0T6bIk/8uTVfn0bwDx/zNKl/ojdt7nslT6Puy594GVbgGMwwfleYD+87AOh5kmkF6LZTxmCPoUfYAYoqNswgIjn3oSpmvorXrak0V3w3IMwA9D7QD1edj2eOwIzIEIDpYypKlIKzx0LbA08bQOEiEjJotwlagEmIMcxDbrm2a4NQSC+qhfHegAzqMgQTMn7bcyg5Q2Yqq7by5JjES+7mD5OFSoiEt3qa1MynmfXgufKX8YE6Ufwss8V/VzxY/mYQAywvR3X+gK7flVYs4GIiIgUim5QNoLWklviuXWY0i1AX8beDma1+RhwIqbF9QI6P2cWEREJRdSD8ms23R4zY9M4zHPm2/twrMds+nHyXZ9+gZfteooQERGRCoryM2XIz7K0O3C4Xf5NuzFtSxcE5WBqxWXkx8wWEREJXdRLyg/b9FDMtHKrMbNB9cVs2o+hncLLLutH3kRERMoq6kE5mL848AO87KI+Hck09joe02r7dPoe3EVERAZEdPspBzz3I8ClmK5L3+5j1bWIiEjkRT8oi4iIVImoV1+LiIhUDQVlERGRiFBQFhERiQgFZRERkYhQUBYREYkIBWUREZGIUFAWERGJCAVlERGRiFBQFhERiQgFZRERkYhQUBYREYkIBWUREZGIUFAWERGJCAVlERGRiFBQFhERiQgFZRERkYhQUBYREYkIBWUREZGIUFAWERGJCAVlERGRiFBQFhERiQgFZRERkYhQUBYREYkIBWUREZGIUFAWERGJCAVlERGRiFBQFhERiQgFZRERkYhQUBYREYkIBWUREZGIUFAWERGJCAVlERGRiFBQFhERiQgFZRERkYhQUBYREYkIBWUREZGIUFAWERGJCAVlERGRiFBQFhERiQgFZRERkYhQUBYREYkIBWUREZGIUFAWERGJCAVlERGRiFBQFhERiQgFZRERkYhQUBYREYkIBWUREZGIUFAWERGJiETYGdgoeK4DTAXew8v6YWdHREQGJ5WUy+PHwLvAjWFnREREBi/H91Ww6xfPHQKsBOJ2yw542VdDzJGIiAxSqr7uK8/dBxgJrCUfkAGOABSUpXI892BgPV72kbCzIiL9o+rrvvDc3YBHgX8DMzu8+6nKZ0iqluceBtwLPIzn6twTGeQUlPvmVMCxy/vZ9H6bfhzPrat8lqRKfbFg+ZywMiEi5RGdoGxaMA8WuxfZdjewBKgDdq5sdqSK7VGwfCieOyK0nIhIv0UjKHvud4AsnntB2Fkp0Q42XVawbQHwlF3es7LZkapkGhlubteWYm4IDwwvQyLSX+EHZc8dDVwJjAB+gudOCTlH3fPc4cAou3ZdwTsfoqAslRX8VlYBt9nl/brYV0QGgfCDcuc7+xNCyUXpggthFvhXwfbCoLxXRXMk1Wozm84B/muXFZRFBrEoBOUdO6x/NpRclG6STT8gH4QB5hWsb4PnNlQyU1KVgnNxLqY3AMBHbbW2iAxCUQjK29r0pzbd01ZpR9VYmy7Gy64FDgZOwMsuwMsuBt6x7xdrDCZSTsG5+CGQwdwY1qDHJyKDVhSC8lSbPgrMxnQ1+nh42enRGJsuBsDL3o+XvaXgfT1XlkoJzsUldsz1oLS8b0j5ERkcPHcMnjsx7GwUE4WgHPxh5gMP2eUDwslKSfIXwuIUlKVS2t8gwv9sqqAs0hXPnQq8BbyP554cdnY6Cjcom77Jm9i1+cCDdnljCMp7DbK+1zL4dDwXg6C8D54bhRtukSj6CtCAGR75Kjy3NtzstBf2D9fF9K0E08/3Ibu8M547tugnwhd0h1rWxfvPAW2Ym41JXewjUg4dz8VZwGrMBWe7MDIkMggUDke7KXB8WBkpJuygHNzpr8LLrsPLLgJesds+GVKeejLSpiuKvmsaf71o11SFLQOp/bnoZVuBJ+02dY0S6chz4+QHf7rZppHqhht2UA7u9JcXbHvApgdVNislC4YxXNnNPnquLJVQ7FzUc2WRriUxtbPrgB/ZbYfYQaEiISpBubAq+D6bHlzhvJSqlKD8JKYleVdV3CLloKAs0jtJm76LedzzFlAPHB5WhjoKez7lYkH5Ycwz2Wl47mZ42TmVz1a3ggth8epr43q87B8qkRmpasWC8hOAD2yB507Ey86vfLYkdJ47HdNn/SbbXa46ee4o4HzMqHe/xzxDBpiLl/Xx3NuAbwOfAf4WSh47CK+kbOr2r7Vr+aDsZbPA03YtilXYwXO8rkvK1fwjkMowLfuDKrf8DaL5/bxg16L4+5GB5rkucDXwR+CkkHPTM8+tx3P/hed+iOd+ucxHvx74HibWnE++8e08mwZjxjfiufVl/u4+KWtQTqbSJyRT6YeSqXQpsz2dSP5O/5UO70W5CruU6mupsGQqfY49904JOy8VMoz8nN4dz8X/2PSQymVHIuQzmNHe3iQipb8enA40YvL8Kzy3PFPfeu4E4MjCLeSHdQ6C8jOYIZOH075VdmjKFpSTqfRmwE2YVtM/SabSPXXKnm7T24CZHd6716aH4LlhV7HnmTupoHSSDTMrkpdMpfcAfok5925IptJR7udeLkGXwRZgbYf3gqD8afVXrkpBYebPeNmWUHNSmuMKlmPAD8t03P0xN66zMLVHI4HP2feCERl94B9222fK9L39Us4f7AmYZxiBHydT6aFF9zRVb5+wa9+33YgKPYYZEGEM0erasT3mP3kpZrxhiYbC0nEM+EUyld7Yg9FONp1d5HHJY5jpHMcDu1U0VxIFu9r0ye52igRz07i3XTsSyGFuJstRWt7Fpk8CV9nl4LqwtGC/v9v0KDy3MIaFopwXrmC86m9jWrZNAM7tYt8pmIFDWoGXOr1r+lveadeOLWMe+yvo3/ainhtHSnDunYWpwdgR83hkY5Y/FzvysuuBe+xapAZGkAFmgso2dq3ztTV6tgCGYroo3U3+Ge/Xy3DsYACdl4BbyVdZQ/ug/Cim5DwaU7oOVTmD8kdt+hhwmV3+VjKVLtb/K/hjvWEvIMUEVQrH2UZhUZC06Tvd7SSVk0yl68iXGu8BfmyXZ2zkpeWkTbs6F/9q0xM13GtVmYQZPnI9ZkrPqAtuIF63hbHg93sSnrtpF58pVdKm79pq/F8XvJcfJtl8b3Az8IV+fme/leWilUylh5Fv1fYy5tnyW5jq57OLfCSYGerdbg57D6ZV9iQi8gCefL7fCzUXUiiJ6dq3CtPt4ReY1sjbA0eHl60B19O5mAbWAJujQWyqSXBezMHL5kLNSWmC/GYA8LJPYkquNcDXynps+C3QbJc7dhX8vU2Pw3Mb+vm9/VKuksQWNl2WaWpcnmlqbCXfeOvCIqXlzWzadXDzsuuAG+3amWXKZ39NtmnU+k5Xsy1t+k6mqdHPNDVmMY2+AC5KptIbaykxOBffL/qul10N3G7XvlSB/Eg0dH9eRE8QCwqvqT+w6Tl9ngPBc4eSHwfD/C287IfAUcAZeNm3O3ziaUyBsh44tU/fWSblCsrFSr43A29jWol+pcP+U2za04nzO5sejedu3q8clkfhpPISDcXOvZ9hzq1/Ev4AOQMlGDe+u3MxuDk5Cc8dN8D5kWgo5byIkqCG9YOCbWngeUy3v/P7eNzg79BCYZdBL3sPXva6TnubNkJB9fY3wmzwVa6gPN6mC4INHUrL30qm0qML9i/txPGyL2O6R8WB75Qlp/0T/BuWdruXVFKxc+9DYPNMU+PlmabGwdAlpHfMM+KephAFM7rXM5ixfvtbFSiDQynnRZQE19TFG7aYABm0S/oqnjuxH8dd2otGuX8AFmJK76GNd1CuoByUIBd32H4T8Kp9//sF23sT3C616Wl47vZ9zmF5KChHT9FzL9PU2BZCXiplKBDMAdv1uWguRlfYta/juZO73Fc2FoPtGtVVfv+JqVIeQX7iiN7o/c2JeWQafNeVeO7I7nYfKAMalG1pOai6PiuZSh9hl4O6/p5PHC/7P0z3qARwbWgtsc0gJq5dGyx3odWgqxvCjVlwIWvBzJ/cnTuA/wJDgP+nltgbvY0jKJtGamdjxnE/Cc/9bFmO27OrMSOhbUL5BjHplXIF5eB5Vafq6ExT4yOYFrEAf0ym0jvS+z/YOZjnAh8jpD8U+RsJaD/VpISry3NvIxb8fpb0WDVn3j8X0+r0COCbA5s1CVn+3Bgcuo4FXvYZ8oN+XIfn7lqW43bHyzZjCpI+cBaee1qvPl8GA119HbgQeBwYBf79vr+haqG0qQ297PvAGXbtAjz3ihDu+IP/5Kzt1ybRUI0l5eD3U9oFx8u+CHzDrl2F5+7d3e4yqPXu3AiTuYb3VGs6AzNz4AjgXjz3YyUeve/P1r3s/eSfaV9b6cBc7pJy0QtjpqlxPWbA8ReGsW684xBUQZd+4njZvwHfsmsXAffguTt084lyG2wNKKpFt+feRqovpYBfAw/a5Y93t6MMaoOp+noEbIgFxQtopgB0NKbB4ljgYTz3Sjx3RNH98/r7d7gMM7NUDPg9nntjpdpklLuk3GUVYqapcRnwyU2dJXcDNPs1bL7upu8kU2m3q8904mV/iJlRZC1m0PWX8dy78dyz8NxtBvh582A62auC7YNcjd3Uen8ummrsYCzkzbrbVQa1wXSdCvK6tsj8B3lmOtIDgFswg4p8B/gAz70Gzz22i+5+/StEmWfaX8IEZx/TGvttPPdveO5JeO5mA1Vb2+s+nNfMOOmwUaxq99B9enza5BdzW/Cqn+y2tJJpalzResmoi4BDlzMMn9gM4BvJVPpnwM8zTY0LYcPFthbTyrTD609zPhN75PyvJf5++tTYor0w09MdAtDqx5pXXbLph6upz67yh2TXUbt2PTUtzX5i/XpqWtaTaAXHz+H4vn3l7Kv9dvB9p92zum1jyS13imVY69euGtLbP1oZ/XLGqVuNZ/lFIWYhMr6fiCWe86fVPp3bhow/sWIl5b/NOOo68tMmVtxusU13nBabx1q/dmVP56KdFMYF3Ftqt3H2iL2O7zO1z5n3XOe2to9PWekP2QYY7YDr4I908EfEyQ1x8BMOJBz8hA9Mn3lHsRH9+uXGGcddVkeLWpJ34OA7x8dpcBxY69cuGajr1HUzTvy/Eazdt9h7yxh+y5dm/unfJR6qxxuIZCpdA7jwp4YErVf9sOaa5z8de+bsYU7zZMxY92cBNF8yZtUKhi1c69euWkP9qm1j7Auw1q9dMaT98RyATFNj0bYY9vvGAWPhT0OBB85P3Lr4lPh9Z411VuwIfNa+aPHj61ZeMunDFf6wZ5KxhZ/Fy5alx4fj+72bV+GWGUfe+NnEI536cK3267ii9ZTJV17xk+7HW/Xcg4D7sv7Q93dpvnYF+YH1IT8EWg0llOI3d+ZzWOwpDoo/x/bOewxxuhpGu3z+0bZv7oKWc/6BGZt7KSavPqafbANmLNetbXpLpqnx2nJ+/+9mfG76mYm7/lzOYw528/zR/qbO0nilJglpvWSUn3DCH8Hwt62NLVe2nvwXzGALrZiBGKZihh7d3L4agv33j73A9bVXsdIf8vaISxds1dPx51+SrH8xt+UX6miZPspZuc0EZ9nYMayoqXFKu/a0+HFqLl1a9puX176308rtYnOKjakvQKsfY9vm6xe2kngfOC3T1PhyOY9/33f3f+Hg+PO7FHtviT8id2PbpxLnz/xDj7/FBZckj9jEWXbnar/urR2a/3AKZnS+rYGtMOfuFphW0O045PhY7FUOjT3NPrFX2Co2r+MuG5y5/uvr7s3tHowP34ApQS8HHsBMgjESE4TH29fozkcB8NneeY+j4o/xsdir7OBkKLwG/LttjxMOu/y+W3r6N5ei1yXltdTNfia39aLCbTs574wd5jTHrqy5bix0E5Q9dxq2JbbrrMlg5r89DvOMeDfMIAcdtWG6fazp8Fr3rj8x9+u2o/1ftx3tx2nzt3PmDJnqLKyb4CytHe8srx3O2kQdLbF6Z32sjpZYLS0xB3NH6eATw3ewqeP4OODE8AG/6IVktT+k7trWRtfm+bhi+3TwPua5RNm0EVvY8e9fzXaPvTF+U2epg/kxVeR5//P+VgvN6RKepf6IITe1HTwC+Lx9dScHZOf7o+uBITW0Tupu5ze+t8O2Kxj6m+2dNZ84JP5M0X/nCn8Ia6nLrfdrci3E29ZT09pKvM3WPuWC9CN9+td1731/3OzVuXpVwReRw4ml2/ZuaCUxATNTX9nP0+WMePmZ3NadBvTYzXlz/BhnZSxObhfM/MVFXXbxufvuEZv9m31izTsCvJjbcivMQDfdWYkJpmt8Yusfy+3Y8lhuxxagZThr2rZ15tRtEVtQP5oVNaOclTUNrK75wB879sHcriMw4+AXmkB+XuVicphryWpMQbEZnOZX/WTzq63JVqBtCOucLZ15tVfWXLf3zrF3E2up2w9Tvd5vvQ7Kp8689QryAxIYnvs4Zk7MrTATStPh/TrgYsyzgOA7H7ZVCLcCt9pJLcZhSp3rscE3aiMyfTGV3hkzk8jumIYK6zFV7eMwJ84b9vU6pnFCWX155s0PYk4qAfDcDzAlxK2oUFDe47KnO929V5qthtsLM07vTpjf1VzMGMLvFrzeB1Zmmhr9PVM3XQp8r95pqcdzR+JlV7Q7qOcmXs1NvWZLZ8FpdU6rA7DQH8Ws3BZzFvkNzy1n+KMr/GHPLPQbXv75ld9fEsrICsCnL39gj5C+elA4wTyy2BFT8utu0p8+Of7yO4uOdjX3ki1aJrEkUc/6fSgSlKdf9MO6o2KP3X5h4tFDg1rNRX5D7ua2g5ZjAmAGc918CzP72TuY83mZHfOiV5KpdBxT8g5uIJbb17aYGtoEZvKaxcAizGhei4ClmabGkqrC7v/ubW8CWw1l3XY97lyico0L/Bb5oNye547GDGCwn93yb0xQf6xwt0xT42p6HgghdJmmxsLuJRK+t8gH5ehP6l4m9ob2CXouYWywiFGvLvQbmOAsBzMB/H83vOm5Q9/JTXxi+9h7OwE8mdu2+f623a75W9sBM15oOnFl0QNKJGWaGtcAT1X6exf77opJzpLRw1m7W8f3zr1oxoRz4w++vF/85bEAs3NTlr2U23zmUKf5Z7+8cmbul50P1292VL/X7KvQO8Bd5fiOFQydA2w1grXJchwPyhuUoWNQNjN8PIypPsgCZ+Jly1LEF7HewjwG6XjuDem2RWd1evvZ3NYcHn8KYF+CoOy5oz/wxz63RWz+1HV+DT9p/eyjf2k74IgXm07IqkWhlGq5P3wBMHq4s3abwu1nXeRNPCtx9xu7x94c3uwneCC323WHxZ8+c9vLXq5IG5CBtMof8hpw4Chn5fjC7V+86Iraac7cYy6+4td/6+0xy9UlKgjKW2/Y4rlDMOOXbo+pVttPAVkGQHDuTduwxXM3AV7Bc7+mYSXbefvZnPkztfjxTwDguZOX+8NmTXYWT836Q5nRevp1v2074hMvNp2QDTOjMvisZGgGwGX1huf9+33n+rrp8Qdn7R57c/hqv87/T26PMw67/L4zKtUoc6CtYshzABOcZRv6TSdTaefY+KNPfj1xy1+vmzH9mt4es1xB+QWb7o7n1tj+wjdhhsVcDnzazvgkUm4v2NSMUmVuBm/HtN48G9OVTjBjBTyT22YxQJzcQXjuUev8mmcanNWTF/ijOK/lvOt+dMVVZ3TVXUSkO2v8uqcBJjmLJ4IJTl+J3/HUgfEXxrX4cR7M7fp/R11+d+dpEwexef6Yu1v9GGOclbEbZxy3C8B3EzfecHT8sV2HOOsZ5yxf0dMxOipXUH4N87B8KLA/ZlDvz2AaQR2Nl321TN8j0tFjmO5AW+K5OwJ/wjSAWgYcgZeNfDuFSnrR3/LBWbktiDl+LXBHvdMy4e3cRE5dn7rrkdwuZ4WdPxm8ap3WGwE2dxbUpi7+xh5fi9/295MTD+wM8Ghup58ecfk9fwg3h+V3+RU/n/eOv+lagHrWf+WnF5+eOiV+3+cBHmzb5YGjLr/7wt4eszxB2Yx+ElRN301+QO/P42UfKct3iBRjWhAHgxXMAo7BdGM4Bi/7ZljZiiof545vtnyZOblxuVY/xp1te3PC+u8984Y/5fhSW5yKFHPM5Xe9/XZu4oqY43NU7LFHzk7ccQzA07mt7zng8oe/HnL2Bswb/uSnAD4Zf/HM0xP//n6d08LzuS3fW0P9p/pyvHKVlMHMl7zEHrMF06ir1w+5Rfrge5ihV2OYFvyf1c1gl25705/8zifX/zS2XfP1nNfy1dlLcA/LNDWqUZz02xx//G8A9om/Wl/ntPJabrNX9oi9cXjY+RpI7/vjzl3l1zPeWR5znTW8npu8dIU/bKfGy+/p001ur0f06pYZsPsQ4H942dnlO7BIDzx3a8xEC/fiZeeEnZ0oS6bSm2Mmd1kGNGWaGnv93EukKM+NvZjb/MZxTvaQD333np1j734RLzvwQy2G7C8zjj5x89iCS1f5Q2ZPcJZ9bsfLZvX5Jre8QVlERET6rJzV1yIiItIPCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRibAzICJS7ZKp9KHABcDVmabGf4WdHwmP4/t+2HkQEalayVS6HpgPNABrgC0zTY0LQs2UhEbV1yIi4dofE5ABhgKfDy0nEjoFZRGRcO3WYf2UUHIhkaCgLCISrh1s+iOgDdg5mUpvFmJ+JEQKyiIi4Zpq06eBJ+3yISHlRUKmoCwiEq7JNv0A+I9dVlCuUmp9LSIbpWQqPQ0YlWlqfCrsvHQlmUrHgHVADabEPBF4AsgCYzNNja0hZk9CoJKyiGx0kqn0XsArwJPJVPobYeenGw2YgAywEHgGWAq4wJ4h5UlCpKAsIhujC8kHu8uTqfT4MDPTjTE2XZVpamzONDW2AffZbarCLhfP3RfP/TOeOz3srPREQVnC47l74bmv4rkP4bmbhJ0d2TjYKuGDCjYNAc4PJzc9CoLykoJtwXPlwyqcl+jz3APw3Nfx3P/guaNL/MxI4F/AdODPeO6+A5nF/lJQljD9FtgO+KRdFimHqZhq4WbgRLvtjGQqXRtajrq2uU0Lg/K/bbpHMpWeWOH8RJfnxoDfA1sDnwZ+XuInG8kPzgLwczzXKW/mykdBWcLhudOAnQu2HInnfiKs7MhGZXubvgH8A5gHjAOODS1HRSRT6U2BP9nVDUE509Q4HwgapzVWOl8RthuQLFg/Bc/dtYTPfdymN2Aa1X0UiGxpWUFZwrK3TR8FrrHLqZDyIhuXrWz6RqapsQW41q5/JaT8dOWCguU7Orx3p02PqlBeBoOP2fQu4C92+dslfC4YMe1u4Ea7fEEX+4ZOQVnCsotNnwd+COSAw/Dcnbv+iEhJJtn0fZteizm/PplMpbcLJ0tFHW3T4zJNjb/q8N4/bfqpZCo9tIJ5irLgmvEc0GSXT8Bzt+jhc9Ns+ir5Ku9j8NxIjpqmoCxh2dqmr+Fl3wZutesXhpQf2XgEg3HMBcg0Nb6PaegD8KVQctRBMpUeRT5YPFRkl5eA94B6VIUdKLxmzMI0iIsBX+/yE547gnxjugxe9hXgAfu5Lw9cVvtOQVnCEgwtmLHpVTb9HJ47tfPuIiXb1KZzC7b9xqZfiEjJM6gRei/T1Li045uZpkaf/PPmL1QsV9HW8ZrxA5uejueO6+IzSZsuw8uusMu/tOmZeG59WXNYBgrKEpbgB/YeAF72WeB+IE6En/fIoBB0lVlcsO0/mIt5A3BChfNTTFDl+kY3+9xg00OrvhW25ybI14C8Z9OHMIOtDAHO7eKTQf/0+QXb7sQ82hhLNM6FdhSUpfLM3alr1+YVvBPc+Z6J545BpG8abLo82JBpasyRb1AYhWrLLW36dlc7ZJoaXwcex9yoRqLaPURjMH8HH1gAgJf1yV8zzsVzhxX5XHCDlu9y5mVbgf9n1y60Xa0iI1KZkaoR/FDaMGP8Bu4DXsBM9B61lrIyeDTYdHmH7X8AWoC9kqn03oQraIw2p4f9fmbT85KpdLGgUy2Ca8ZyvGxbwfZ/YG5sRgOnF/lcscFZwDzOyAI7AseXMZ/9pqAsYQh+KMvs3a5hloNny18recQeESuZSieAEXZ1eeF7mabGhcBNdvWHyVQ6zAEkxtr0wx72u4180PnqgOYo2oJrQfvn7yZA/8iufQvPHd7hc8G1puPnlpG/4bkiSs+WFZQlDJ2rlPJuwXRdGIvpKiXSGyMLlrNF3v8usBbYDzilIjkqLgjKi7vbyY6FfaldnZFMpacMaK6iq7trxg2Y58yTgcs6vNdVSRngJ5hnzVsRoTESFJQlDMXveiF43nOWXTsdzz2x0z4iXWuw6ZpMU+P6jm9mmhrnAjPt6q+SqfSWHfepkKC1cE8lZTCl+0cxj3VusLUB1aZ4iRfAy64l307gfDz38CKf6xyUTWvs8+3ajKiMKKigLGHo+gcG4GX/R35wgD/guftXIE+ycWiw6fJu9vkB8D9MNffdyVQ6jMlQSiopw4buUWcCq4ADgKvtpBvVpOsbeQAvezem8ZaDmXRizw6fK1ZSBlMzdzOmEdlteO5OZcltP1Tbf6xEQ/c/MGMGkMZ0d/g3nvvFKA8iL5HRYNPlXe1gq4RPwHSR2gp4IplK7z7QGQskU+ka8r0PegzKAJmmxtnAaZjWx18Gbkqm0iO6/9RGpZRrxvnAw5hHGPfhuSfQffV10I7lS5iuVWOBh/HcUAdrUVCWMPR09xo04DgeM9xgPabl7EN47sF4bnzAcyiDVYNNl3e3U6apcR7wKeAtTJ/5J5Op9DUVGoYzKCXngGWlfijT1HgrZiCRNuBzwOvJVPrsZCo9svtPbhRKuWasB47AjNg1Avgr+fGyuw7mXnY1ZtapJ4BRwL/w3Dvw3I+FURBwfN/veS+RcvLc3wFnAN/Dy17ew75x4FvAJUCd3boAeBAzk85bwLuYZ3NZvGzzAOUagJ/NOM2J4Y/teU8ByOEsPn/mHyp2kUmm0qcD1wF3ZZoaeyzxJFPpMcAvgJMKNr+KGcjmZWA25nxbDCy3/Z37m8edgBeBDzNNjeN72r/I5/fFTKwQTPu4DngEUyX/OvAOZnCMJXZCjsHPc/+Kqd34Gl72Fz3sW4O5XnwLqLFbd7RDbHb3uTrMY7PzMNXZYGpTHgSexbSCfxcT4LP2JqDseh2Ub57xmabdY6+fNxCZGazWUrvuidz2n/3yzJsfGOjvumbGyYfuH3vhtoH+noG0qbOkboSzNv6Ptn3/ckHLOddgSgxDgOH2NaxgeTgwbEtn7rgLErfuun9s1pThzrouG7qs9xP+Wmrb1lOTe98fN/8jlz2bLGfefzHji+O+mvjHonIec2P2i9Zjx3915vWlNGYq2cPf3e+NTZxlk4q910Y8sYa62pdymz9/WuI/52HmVK7HlJyG27TwNRwYsdBvSC70R227hvpxDuaa6ODbl1kGiJPLOfi5GDk/hu/H8HNs2B8IVjrx/aDItY7aoTli3Nx20NwfXXHV5OL7dy+ZStcBZyVoPXuy8+G2Wzjz2dRZwihWMspZheusZgjN3NO2+7E/u/L7t/flO7py+3cPu3M7Z86B5TxmTyY7H9YNc5rjN7UedO2M1v+7CfN3LrxOjOi4vJ3z3oSvJv6+01Ca153R8o1vtVAzDxNsO54D7V67Om9OPDdx+w77xV6eUO+0dFmb3OzX5NZRk2sj7rcS89uI+W3E/TY/Rhtx3weezG179Ukzby9lJqsNet2Kr5aWSdvEPojC2LFRMnSuP87DVJsMqBi5ho3l7/+3tv2nA9NL2fdtfxLntnyNWlr4aOwN9nRms03sfTZzFjHFWYTrrAGg1ml1amlNACz0G9TPeSM03slO7Ok3sHvsjd0wLZZLMsFZzgRneSm7xijTY7+xZIfnu+X3gueOyNQzHTjE95nkdFPButIfWvbq1+GsnRLWNejPbQeegall69Fr/lS+0rJhxN6/l/odL/jTOKPlQoayjj1js9kzNputnLls5ixikrOYEc5aAOqcllgdRYJ2wV/8udy0Xg+P2uug/CHur/7YevCbvf3cxmoz58Pp+8dnbeeyuiL9B9dR+78/th58SSW+a+A4zlO5bSc8kdt+G/IjG60BVmNamK7qank9Nasfz+2w/nF28GnDx9wx+/U0O0lnYf1mzsIh453l9UNZVxMnt3jHMuc8h7P4F63H9rrKsVrlcEpqyNQbj+e2/8bTua27bDE9zZk7Zq/Ya9vFHKZhSkbNwMoOr1VdbGuB/HkVvFb79fEP/LHDVzK0dp1fm1hHTbyZ2kQzNTVtfgwfx/HBseVrB/NBx26HDe/DWCc76tD4MxdMdj6swXOddgPodMcMcHE+cBF2gBQbkNdhxtB+B1PNvqTVjy1byojc3rFXnyzp2L3wjr/p9/7YOmbXch+3Oz6x2KO5HTd5xd98WyD4vy+8RqwsWC5cXwfsaV8NmOfxxf7vO50La6hvfSi3q/9QbtcctLvWsLkzvz7pLBza4Kyqq6MlVkeLU0dLvMZpjSdoi9fSknCAVQy5t7f/Vj1T7qc/zTjmByclHvzWq7nNVm1/2UvV1BpSRPrCc2sxN6FxYFO87PwePoGdM/gf5GeXegPT+PE/wCy8bL+fdUs0VGMn9LJqpvZZgAnOsmoel1ZESuVl1+O572FmitqK9jMYFdnf3Q4zI9J4YBHwTeBmBeKNk7pE9VMNrf8FGOOsdC67+JyijU9ERDp4y6ZbdbuX544H7sUE5BeAj+Blb1RA3ngpKPfTKTP/Pn+5P8wHGOWsOhggmUrHLrgodWEVjrojIqUJ5lHuutmDmVLwT5h2F68Dn8LLzh34rEmYFDTKYI4/fjHAGFYcCfDNxF9v/3HNb666MnHt8+HmTEQi6gmbfrKbfY4HDsI8f/4MXrbsjeYkehSUy2CBP/oZgKSz4MBLLj7vJ2fG00fGHJ+pzsJ3ws6biETS/Zj++R/Fcz/RaeQoMwBGMHHGD/Cyr1Y4fxISBeUyaCHRBLBP/NVR303cdEGd08oruamz942/clzYeRORCPKyCzBDyIIZr7njlIOnAdMwI9X9tII5k5ApKJdB4+X3PPJSbvOnARJOjrdzE9+d4Cz7qBpjiEg3CkdGPHjDkucOxQwTCTATL7uykpmScCkol8lOsXf3fTO36dlv5TY9a8vY/GljL31/Tdh5EpEI87IfAB+1a1sUvHMusCnwHnBNpbMl4dLgISIiYfHcBvIzRbmYgtI7mNmKvoCX/WNIOZOQqKQsIhIWL7scCLo57QxciAnIrwA3h5QrCZGCsohIuJ616XTM2NYAF9s5xaXKKCiLiITrHpueAwwF/ku+ZbZUGQVlEZFw/RkzuxOYmY++VPLMUbLRUUMvEZGwee42wJHA3XjZl8POjoRHQVlERCQiVH0tIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEaGgLCIiEhEKyiIiIhGhoCwiIhIRCsoiIiIRoaAsIiISEQrKIiIiEfH/AX30XVZ0OSQ+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(dpi=100)\n",
    "plot_voltage_traces(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.859375\n"
     ]
    }
   ],
   "source": [
    "output,_ = run_snn(x_data)\n",
    "m,_=torch.max(output,1)\n",
    "\n",
    "# Compute training accuracy\n",
    "_,am=torch.max(m,1)\n",
    "acc = np.mean((y_data==am).detach().cpu().numpy())\n",
    "print(\"Accuracy %f\"%acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
